AI Will Help Leverage IoT at the Edge

Summary:
It's a given that the number of devices is exploding, and the signals, events, and patterns coming from these devices are just overwhelming. The speed of response to all of these devices and signals is the key to digital success. Imagine what you could do if you put the intelligence closer, and even in, these devices? Moving AI to the edge will be a major movement starting in 2017. This month Jim Sinur shares his take on the opportunities such intelligence might provide to augment management in the digital era.

It's a given that the number of devices is exploding, and the signals, events, and patterns coming from these devices are just overwhelming. The speed of response to all of these devices and signals is the key to digital success. Imagine what you could do if you put the intelligence closer, and even in, these devices? Moving AI to the edge will be a major movement starting in 2017. What might this intelligence help? Here is my take on the opportunities to augment management in the digital era.

A Listening-Post for Patterns

With a multitude of interconnected devices emitting signals, the amount of electronic babble will be more overwhelming than the number of devices or the amount of big data. Devices can emit multiple signals. Not all of these signals will make a central big data lake and must be dealt with at the edge. In fact, the patterns of multiple signals deliver the best benefit when caught close to the source and dealt with immediately. Only the signals and patterns of regional and global impact need to be passed up to a central management point managed by processes, applications, and humans when there is no way to deal with them locally. Adding AI to the edge will push the envelope for "hands off" handling.

A Bidding Service for Work

With multiple controllers and devices listening for service signals, there will be opportunities for multiple things to respond to the needs. AI will help sort out which device (or device network) will respond to the needs. AI interacting with AI can determine which devices, applications, or processes will lead to optimal multiple goal fulfillment to the goal-driven digital world. This is already happening in smart and parallel manufacturing with bots.

A Change Configuration Assistant

AI can be the smarts in figuring out if a change in device code is needed, signaling a development source for upgrades. In addition, AI can trigger smart development and parameter-setting on the fly through robotic programming assistants (RPA). We can see this emerging in 2017, with smart configurators and auto PROM changes in devices or in the cloud. This can be leveraged in the software world as well through RPA in website, processes, and application components.

Net; Net

While the visible aspects of AI will be felt by the consumer at the digital assistant level, there will be a quiet revolution going on as AI finds its place in Industry 4.0 and other large scale digital efforts. Watch this AI space leap forward in 2017.

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About our Contributor:

Jim Sinur
VP and Research Fellow, Aragon Research

Jim Sinur is an independent consultant and thought leader in applying business process management (BPM) to innovative and intelligent business operations (IBO). His research and areas of personal experience focus on business process innovation, business modeling, business process management technology (BPMT), processes collaboration for knowledge workers, process intelligence/optimization, business policy/rule management (BRMS), and leveraging business applications in processes. Mr. Sinur was critical in creating the first Hype Cycle and Maturity Model, which have become a hallmark of Gartner analysis, along with the Magic Quadrant. He has been active in the rules, data and computing communities, helping shape direction based on practical experience. Mr. Sinur has vertical industry experience on the investment and operational sides of the insurance and financial services.

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